class_docstr: Confusion Matrix for segmentation evaluation.
  
  Args:
      num_classes (int): Number of categories of the confusion matrix.
      streaming (bool): Whether to use streaming mode. If the value is set to True, the data will be
                        accumulated every time the `calculate` interface is called. Default to False.
code: def kappa(self) -> float:
      vji = np.zeros(self.num_classes)
      vij = np.zeros(self.num_classes)
      for j in range(self.num_classes):
          v_j = 0
          for i in range(self.num_classes):
              v_j += self.confusion_matrix[j][i]
          vji[j] = v_j
  
      for i in range(self.num_classes):
          v_i = 0
          for j in range(self.num_classes):
              v_i += self.confusion_matrix[j][i]
          vij[i] = v_i
  
      total = self.confusion_matrix.sum()
  
      # avoid spillovers
      # TODO: is it reasonable to hard code 10000.0?
      total = float(total) / 10000.0
      vji = vji / 10000.0
      vij = vij / 10000.0
  
      tp = 0
      tc = 0
      for c in range(self.num_classes):
          tp += vji[c] * vij[c]
          tc += self.confusion_matrix[c][c]
  
      tc = tc / 10000.0
      pe = tp / (total * total)
      po = tc / total
  
      kappa = (po - pe) / (1 - pe)
      return kappa
  
imports: import os
  from typing import Callable, Union, List, Tuple
  import cv2
  import paddle
  import PIL
  import numpy
  import paddle.nn.functional
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 6 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.kappa() and returns: kappa